Virtual Screening Using a Ligand-based Pharmacophore Model from Ashitaba (Angelica keiskei K.) Isolates and Molecular Docking to Obtained New Candidates as -Glucosidase Inhibitors

http://www.doi.org/10.26538/tjnpr/v8i1.15

Authors

  • Anne  Yuliantini Faculty of Pharmacy, Universitas Bhakti Kencana, Jl. Soekarno Hatta No.754, Bandung, West Java, 40617, Indonesia.
  • Syitha Ocktavyanie Faculty of Pharmacy, Universitas Bhakti Kencana, Jl. Soekarno Hatta No.754, Bandung, West Java, 40617, Indonesia.
  • Ellin Febrina Faculty of Pharmacy, Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km. 21, Jatinangor, Sumedang, West Java, 45363, Indonesia
  • Aiyi Asnawi Faculty of Pharmacy, Universitas Bhakti Kencana, Jl. Soekarno Hatta No.754, Bandung, West Java, 40617, Indonesia.

Keywords:

Virtual Screening, Molecular Docking, Inhibitor, Ashitaba, Hits, -Glucosidase

Abstract

Diabetes mellitus (DM) is a serious, long-term disease when the pancreas doesn't make enough insulin or when the body can't use the insulin it makes well. Type 2 Diabetes Mellitus (T2DM) is a metabolic disorder characterized by elevated blood glucose levels due to insulin resistance and impaired secretion, primarily due to inefficient intestine glucose absorption through the α-glucosidase enzyme. Long-term synthetic drug use can cause issues in the digestive system, kidneys, and liver. Alternative treatments that use herbal products include the ashitaba (Angelica keiskei Koidzumi) plant, which has been evaluated as an a-glucosidase inhibitor. The purpose of this study was to use molecular docking and virtual screening to identify potential a-glucosidase inhibitors from Ashitaba (Angelica keiskei Koidzumi) isolates using a ligand-based pharmacophore model. The screening methods used were ligand-based virtual screening, docking-based virtual screening, and molecular docking. By using 8 training sets of ashitaba isolates, the best model was obtained with 18 features, including two aromatic ring bonds, nine hydrophobic bonds, three hydrogen bond donors, and four hydrogen bond acceptors. The pharmacophore model and docking-based virtual screening simulations of 270,547 molecules in the ZINC Natural Product database and further investigation using molecular docking yielded (R)-N-(diaminomethylene)-3-hydroxy-3-((S)-6-(4-(hydroxyamino)benzyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-5-oxo-2,3-dihydro-5H-furo[3,2-g]chromen-7-yl)propan-1-aminium  (ZINC000085594472), (R)-3-((S)-4-(cyclopentyloxy)-6-(4-(hydroxyamino)benzyl)-2-(2-hydroxypropan-2-yl)-5-oxo-2,3-dihydro-5H-furo[3,2-g]chromen-7-yl)-N-(diaminomethylene)-3-hydroxypropan-1-aminium (ZINC000085594416), and (S,E)-1-(1-(2-hydroxy-5-(7-(4-hydroxy-3-methoxyphenyl)-2-isopropyl-5-oxohept-3-en-1-yl)-3-((iminio(methylamino)methyl)amino)phenoxy)cyclopentyl)-N-methylmethanaminium (ZINC000085597046) as the three top hits with binding energies of -16.09, -15.83, and -15.76 kcal/mol, respectively. In conclusion, the (R)-N-(diaminomethylene)-3-hydroxy-3-((S)-6-(4-(hydroxyamino)benzyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-5-oxo-2,3-dihydro-5H-furo[3,2-g]chromen-7-yl)propan-1-aminium  (ZINC000085594472) was a more potential candidate for α-glucosidase inhibitor.

References

Saelee R, Hora IA, Pavkov ME, Imperatore G, Chen Y, Benoit SR, Holliday CS, Bullard KM. Diabetes Prevalence and Incidence Inequality Trends Among US Adults, 2008–2021. Am J Prev Med Published online 2023.

Andary R, Fan W, Wong ND. Control of cardiovascular risk factors among US adults with type 2 diabetes with and without cardiovascular disease. Am J Cardiol 2019;124(4):522-7.

Suryasa IW, Rodríguez-Gámez M, Koldoris T. Health and treatment of diabetes mellitus. Int J Health Sci 2021;5(1).

Jaber FA, James JW. Early Prediction of Diabetic Using Data Mining. SN Comput Sci 2023;4(2):169.

Magliano DJ, Boyko EJ. IDF diabetes atlas. Published online 2022.

He Z, Zhou Z, Yang Y, Yang T, Pan S, Qiu J, Zhou, SF. Overview of clinically approved oral antidiabetic agents for the treatment of type 2 diabetes mellitus. Clin Exp Pharmacol Physiol 2015;42(2):125-38.

Laube H. Acarbose: an update of its therapeutic use in diabetes treatment. Clin Drug Investig 2002;22:141-56.

Kaur P, Kumar M, Parkash J, Prasad D. Oral hypoglycemic drugs: An overview. J Drug Deliv Ther 2019;9(3-s):770-7.

Rosak C, Mertes G. Critical evaluation of the role of acarbose in the treatment of

diabetes: patient considerations. Diabetes Metab Syndr Obes Targets Ther Published online 2012:357-67.

Caesar LK, Cech NB. A review of the medicinal uses and pharmacology of ashitaba. Planta Med Published online 2016:1236-45.

Enoki T, Kudo Y, Tanabe M, Ohnogi H, Kobayashi E, Sagawa H, Kato I. Insulin‐like activities of chalcone derivatives, xanthoangelol (XA) and 4‐hydroxyderricin (4HD), from a Japanese herb, Angelica keiskei: Induction of adipocyte differentiation and enhancement of glucose uptake in adipocyte. Published online 2006.

Ohkura N, Atsumi G ichi, Uehara S, Ohta M, Taniguchi M. Ashitaba (Angelica keiskei) Exerts Possible Beneficial Effects on Metabolic Syndrome. OBM Integr Complement Med 2019;4(1):1-11.

Luo L, Wang R, Wang X, Ma Z, Li N. Compounds from Angelica keiskei with NQO1 induction, DPPH scavenging and α-glucosidase inhibitory activities. Food Chem 2012;131(3):992-8.

Estrada AK, Mendez-Alvarez D, Juarez-Saldivar A, Lara-Ramirez EE, Martinez-Vazquez AV, Villalobos-Rocha JC, Palos I, Ortiz-Perez E, Rivera G. Ligand-Based and Structure-Based Virtual Screening of New Sodium Glucose Cotransporter Type 2 Inhibitors. Med Chem Shariqah United Arab Emir Published online 2023.

Ahmed S, Islam N, Shahinozzaman M, Fakayode SO, Afrin N, Halim MA. Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-γ agonists as anti-diabetic drugs. J Biomol Struct Dyn 2021;39(2):728-42.

Caesar LK. Bioinformatic Strategies to Understand the Complexities of Medicinal Natural Product Mixtures. The University of North Carolina at Greensboro; 2019.

Setiawansyah A, Reynaldi MA, Tjahjono DH. Molecular docking-based virtual screening of antidiabetic agents from Songga (Strychnos lucida R. Br.): an Indonesian native plant. Curr Res Bioscences Biotechnol 2022;3(2):208-14.

Rigi G, Nakhaei MVA, Eidipour H, Najimi A, Tajik F, Taher N, Yarahmadi K. Virtual screening following rational drug design-based approach for introducing new anti amyloid beta aggregation agent. Bioinformation 2017;13(2):42.

Dirir AM, Daou M, Yousef AF, Yousef LF. A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes. Phytochem Rev 2022;21(4):1049-79.

Blahova J, Martiniakova M, Babikova M, Kovacova V, Mondockova V, Omelka R. Pharmaceutical drugs and natural therapeutic products for the treatment of type 2 diabetes mellitus. Pharmaceuticals 2021;14(8):806.

Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res 2008;36(suppl_2):W223-8.

Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Chem Biol Methods Protoc Published online 2015:243-50.

Adrià CM, Garcia-Vallvé S, Pujadas G. DecoyFinder, a tool for finding decoy molecules. J Cheminformatics 2012;4(1):P2. doi: 10.1186/1758-2946-4-S1-P2

Butt SS, Badshah Y, Shabbir M, Rafiq M. Molecular docking using chimera and autodock vina software for nonbioinformaticians. JMIR Bioinforma Biotechnol 2020;1(1):e14232.

Jejurikar BL, Rohane SH. Drug designing in discovery studio. Published online 2021.

Snyder HD, Kucukkal TG. Computational chemistry activities with Avogadro and ORCA. J Chem Educ 2021;98(4):1335-41.

Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 2010;15(11-12):444-50.

Giordano D, Biancaniello C, Argenio MA, Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals 2022;15(5):646.

Empereur-Mot C, Guillemain H, Latouche A, Zagury JF, Viallon V, Montes M. Predictiveness curves in virtual screening. J Cheminformatics 2015;7(1):1-17.

Pascual R, Almansa C, Plata-Salamán C, Vela JM. A new pharmacophore model for the design of sigma-1 ligands validated on a large experimental dataset. Front Pharmacol 2019;10:519.

Febrina E, Asnawi A. Lead compound discovery using pharmacophore-based models of small-molecule metabolites from human blood as inhibitor cellular entry of SARS-CoV-2. J Pharm Pharmacogn Res 2023;11(5):810-22.

Nursamsiar, Nur S, Febrina E, Asnawi A, Syafiie S. Synthesis and Inhibitory Activity of Curculigoside A Derivatives as Potential Anti-Diabetic Agents with β-Cell Apoptosis. J Mol Struct 2022;1265:133292. doi: 10.1016/j.molstruc.2022.133292

Chen D, Oezguen N, Urvil P, Ferguson C, Dann SM, Savidge TC. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci Adv 2016;2(3):e1501240. doi: 10.1126/sciadv.1501240

Freitas RF de, Schapira M. A systematic analysis of atomic protein–ligand interactions in the PDB. MedChemComm 2017;8(10):1970-81. doi: 10.1039/C7MD00381A

Itoh Y, Nakashima Y, Tsukamoto S, Kurohara T, Suzuki M, Sakae Y, Oda M, Okamoto Y, Suzuki T. N+-C-H•••O Hydrogen bonds in protein-ligand complexes. Sci Rep 2019;9(1):767. doi: 10.1038/s41598-018-36987-9

Madushanka A, Moura RT, Verma N, Kraka E. Quantum Mechanical Assessment of Protein–Ligand Hydrogen Bond Strength Patterns: Insights from Semiempirical Tight-Binding and Local Vibrational Mode Theory. Int J Mol Sci 2023;24(7):6311. doi: 10.3390/ijms24076311

Published

2024-02-01

How to Cite

Yuliantini, A., Ocktavyanie, S., Febrina, E., & Asnawi, A. (2024). Virtual Screening Using a Ligand-based Pharmacophore Model from Ashitaba (Angelica keiskei K.) Isolates and Molecular Docking to Obtained New Candidates as -Glucosidase Inhibitors: http://www.doi.org/10.26538/tjnpr/v8i1.15. Tropical Journal of Natural Product Research (TJNPR), 8(1), 5811–5819. Retrieved from https://tjnpr.org/index.php/home/article/view/3391